Concepts from multiple testing can improve tests of single hypotheses. The proposed definition of the calibrated p value is an estimate of the local false sign rate, the posterior probability that the direction of the estimated effect is incorrect. Interpreting one-sided p values as estimates of conditional posterior probabilities, that calibrated p value is (1 - LFDR) p/2 + LFDR, where p is a two-sided p value and LFDR is an estimate of the local false discovery rate, the posterior probability that a point null hypothesis is true given p. A simple option for LFDR is the posterior probability derived from estimating the Bayes factor to be its e p ln(1/p) lower bound. The calibration provides a continuum between significance testing and traditional Bayesian testing. The former effectively assumes the prior probability of the null hypothesis is 0, as some statisticians argue is the case. Then the calibrated p value is equal to p/2, a one-sided p value, since LFDR = 0. In traditional Bayesian testing, the prior probability of the null hypothesis is at least 50%, which usually results in LFDR >> p. At that end of the continuum, the calibrated p value is close to LFDR.